Hesper Atlas Evidence

Current end-of-day signal lists (subscription)

list_signals

Read-onlyIdempotent

The engine's current lists. recent_entries is the chronological entry history inside the scan window, even if a name is now HOLD or risky. recent_buys is actionable now: still BUY/STRONG BUY and not critical sell-risk. Also supports recent sells, positions at risk, ranked opportunities, undervalued names, catch-up candidates, and the on-deck watch roster. Requires an active subscription. Signal data, not advice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`kind`	No	Which list. Default 'recent_buys'.
`limit`	No	Rows to return, default 20, max 60.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`source`	No
`caveats`	No
`is_live`	No
`citation`	No
`data_type`	No
`disclaimer`	Yes
`provenance`	No
`source_url`	No
`last_updated`	No
`calculation_version`	No
`methodology_version`	No

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive; the description adds meaningful behavior: chronological scan-window history, actionable filtering ('still BUY/STRONG BUY and not critical sel-risk'), active-subscription requirement, and a not-adivce disclaimer. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the core purpose appears first, key kind distinctions are explained, and the subscription/disclaimer caveats are one short sentence each. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists and annotations cover safety, the description is sufficient for selection and invocation: it covers preconditions, semantic differences among kinds, and the data's status. It does not define every list kind in detail, but the enum names are self-explanatory and the schema provides additional constraints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers 100% of the two parameters, so the baseline is 3; the description goes beyond by explaining what recent_entries vs recent_buys mean and enumerating the remaining kind options. It does not add detail for limit, but the schema already constrains that fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as returning the engine's current end-of-day signal lists and enumerates the eight list kinds, so an agent knows what resource it addresses. It does not explicitly distinguish from sibling tools such as get_signal or list_closed_trades, which keeps it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit usage context within the tool: recent_entries is chronological history even if a name is now HOLD or risky, while recent_buys is actionable now, and it notes subscription as a prerequisitive. It does not state when to choose list_signals over sibling tools, so there are no exclusion rules or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0

Disambiguation3/5

The five historical-performance tools (get_track_record, get_walk_forward_evidence, get_heldout_evidence, get_ledger_stats, list_closed_trades) occupy heavily overlapping territory, and an agent could easily grab the wrong evidence artifact. The detailed 'use when' openers help considerably, but the boundaries between replay, walk-forward, and validation are subtle enough that misselection risk remains real.

Naming Consistency4/5

All tools follow a clean snake_case verb_noun pattern, with get_ reserved for single artifacts/reports and list_ for enumerable collections. The convention is slightly loose—get_changes_since and get_ledger_stats are more list-like than get_-like, and the verbs don't always signal collection size—but overall the pattern is predictable and readable.

Tool Count4/5

At 15 tools, the server sits at the upper boundary of a well-scoped surface, and each tool does earn its place in the evidence ecosystem (current signal, lists, portfolio, context, diffs, publications, replay, validation, methodology, provenance). It is slightly heavy and could feel daunting, but nothing is redundant.

Completeness4/5

The surface covers the evidence domain thoroughly: current state, forward publication history, performance replays, two distinct validation artifacts, methodology, and hash-level provenance. Minor gaps exist—there is no tool to enumerate the full covered universe or search across tickers, forcing users to arrive with a symbol in mind—but core workflows have no dead ends.

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